Methods and computer program products for identifying and monitoring related business application processes

ABSTRACT

Provided are methods and computer program products for identifying and monitoring related business application processes executing on at least one networked device. Methods may include identifying a first process that corresponds to a specified business application process; identifying root processes by tracing a dependency path from the first process to each of the root processes; grouping at least one of the root processes into a root group; and identifying a dependency chain of the at least one root process in the root group.

FIELD OF INVENTION

The present invention relates to computer networks and, moreparticularly, to network performance monitoring methods, devices, andcomputer program products.

BACKGROUND

The growing presence of computer networks such as intranets andextranets has brought about the development of applications ine-commerce, education, manufacturing, and other areas. Organizationsincreasingly rely on such applications to carry out their business,production, or other objectives, and devote considerable resources toensuring that the applications perform as expected. To this end, variousapplication management, monitoring, and analysis techniques have beendeveloped.

One approach for managing an application involves monitoring theapplication, generating data regarding application performance, andanalyzing the data to determine application health. Some systemmanagement products analyze a large number of data streams to try todetermine a normal and abnormal application state. Large numbers of datastreams are often analyzed because the system management products maynot have a semantic understanding of the data being analyzed.Accordingly, when an unhealthy application state occurs, many datastreams may have abnormal data values because the data streams arecausally related to one another. Because the system management productsmay lack a semantic understanding of the data, they may not be able toassist the user in determining either the ultimate source or cause of aproblem. Additionally, these application management systems may not knowwhether a change in data indicates an application is actually unhealthyor not.

Current application management approaches may include monitoringtechniques such as deep packet inspection (DPI), which may be performedas a packet passes an inspection point and may include collectingstatistical information, among others. Such monitoring techniques can bedata-intensive and may be ineffective in providing substantively realtime health information regarding network applications. Additionally,packet trace information may be lost and application-specific code maybe required.

Embodiments of the present invention are, therefore, directed towardssolving these and other related problems.

SUMMARY

It should be appreciated that this Summary is provided to introduce aselection of concepts in a simplified form, the concepts being furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of thisdisclosure, nor is it intended to limit the scope of the invention.

Some embodiments of the present invention are directed to a method foridentifying and monitoring related business application processes on atleast one networked device. Methods may include identifying a firstprocess that corresponds to a specified business application process,and identifying one or more root processes that depend directly and/orindirectly on the first process, and are depended upon by no otherprocess. At least one of the root processes are grouped into a rootgroup, and a dependency chain of the at least one root process in theroot group is identified. The dependency chain includes the process orprocesses in the root group and one or more processes that are directlyand/or indirectly depended upon by the process or processes in the rootgroup. Performance metrics for the processes in the dependency chain areaggregated.

Some embodiments may provide that a first process that corresponds to aspecified business application process is identified, and one or moreroot processes are identified by tracing a dependency path from thefirst process to a respective one of the one or more root processes. Atleast one of the one or more root processes are grouped into a rootgroup. A dependency chain of the at least one root process in the rootgroup is identified. Performance metrics for the processes in thedependency chain are aggregated.

In some embodiments, each of the one or more root processes dependsdirectly and/or indirectly on the first process, and is depended upon byno other process. The dependency chain includes the at least one rootprocess in the root group and one or more processes that are directlyand/or indirectly depended upon by the at least one root process in theroot group.

Some embodiments provide that a directed process dependency graphincluding a plurality of processes and relationships therebetween isgenerated. Identifying the first process includes identifying the firstprocess in the directed process dependency graph that corresponds to thespecified business application process. Identifying one or more rootprocesses includes identifying in the directed process dependency graphone or more root processes by tracing a dependency path from the firstprocess to each of the one or more root processes. Identifying thedependency chain of the at least one root process in the root groupcomprises identifying a dependency chain as indicated by the directedprocess dependency graph.

In some embodiments, the dependency path includes following a chain inthe directed process dependency graph beginning with the first processand proceeding to one or more intermediate processes depending directlyand/or indirectly on the first process as indicated by the directedprocess dependency graph. Some embodiments provide that a second processidentified as a root process is disregarded and a third processpreceding the second process in the chain is designated as a rootprocess, responsive to the second process corresponding to a specifiedclient application process. In some embodiments, a second processidentified as a root process is disregarded, responsive to the secondprocess corresponding to a specified non-business application process.Some embodiments provide that, responsive to detecting a cycle in thechain, a Strongly Connected Components algorithm is used to resolve thecycle.

In some embodiments, grouping at least one of the one or more rootprocesses into a root group includes grouping based on similarityheuristics and/or collected configuration data. Some embodiments includenaming the root group according to the similarity heuristics and/orcollected configuration data on which grouping is based. In someembodiments, a policy to automatically update grouping of the at leastone of the one or more root processes based on the similarity heuristicsand/or collected configuration data is implemented. Some embodimentsprovide that the similarity heuristics and/or collected configurationdata are ranked by priority.

In some embodiments, a computer program product including anon-transitory computer usable storage medium having computer-readableprogram code embodied in the medium is provided. The computer-readableprogram code is configured to perform operations corresponding tomethods described herein.

Other methods, devices, and/or computer program products according toexemplary embodiments will be or become apparent to one with skill inthe art upon review of the following drawings and detailed description.It is intended that all such additional methods, devices, and/orcomputer program products be included within this description, be withinthe scope of the present invention, and be protected by the accompanyingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will now be described in more detail in relationto the enclosed drawings, in which:

FIGS. 1 a-1 d are block diagrams illustrating exemplary networks inwhich operations for monitoring network application performance may beperformed according to some embodiments of the present invention,

FIG. 2 is a block diagram illustrating an architecture of a computingdevice as discussed above regarding FIGS. 1 c and 1 d,

FIG. 3 is a block diagram illustrating operations and/or functions of acollector application as described above regarding FIG. 1 a,

FIG. 4 is a diagram illustrating determining a read wait timecorresponding to a user transaction according to some embodiments of thepresent invention,

FIG. 5 is a block diagram illustrating a kernel level architecture of acollector application to explain kernel level metrics according to someembodiments of the present invention,

FIG. 6 is a flowchart illustrating exemplary operations carried out by acollector application in monitoring and reporting network applicationperformance according to some embodiments of the present invention,

FIG. 7 is a screen shot of a graphical user interface (GUI) including amodel generated by a health data processing application according tosome embodiments of the present invention,

FIG. 8 is a flowchart illustrating exemplary operations carried out by ahealth data processing application in generating and displaying areal-time model of network application health according to someembodiments of the present invention, and

FIG. 9 is a flowchart illustrating exemplary operations carried out by ahealth data processing application in generating and displaying anhistorical model of network application health according to someembodiments of the present invention.

FIG. 10 is a diagram illustrating a simplified directed processdependency graph according to some embodiments of the present invention.

FIG. 11 is a flowchart illustrating exemplary operations carried out bya health data processing application 100 in identifying and monitoringrelated business application processes according to some embodiments ofthe present invention.

DETAILED DESCRIPTION

In the following description, for purposes of explanation and notlimitation, specific details are set forth such as particulararchitectures, interfaces, techniques, etc. in order to provide athorough understanding of the present invention. However, it will beapparent to those skilled in the art that the present invention may bepracticed in other embodiments that depart from these specific details.In other instances, detailed descriptions of well known devices,circuits, and methods are omitted so as not to obscure the descriptionof the present invention with unnecessary detail. While variousmodifications and alternative forms of the embodiments described hereinmay be made, specific embodiments are shown by way of example in thedrawings and will herein be described in detail. It should beunderstood, however, that there is no intent to limit the invention tothe particular forms disclosed, but on the contrary, the invention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the claims. Likereference numbers signify like elements throughout the description ofthe figures.

As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless expressly stated otherwise. Itshould be further understood that the terms “comprises” and/or“comprising” when used in this specification are taken to specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof. It will be understood that when an element is referred to asbeing “connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. Furthermore, “connected” or “coupled” as used herein mayinclude wirelessly connected or coupled. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items, and may be abbreviated as “/”.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art. It will be further understood that terms,such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art, and will not be interpreted in anidealized or overly formal sense unless expressly so defined herein.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another.

Exemplary embodiments are described below with reference to blockdiagrams and/or flowchart illustrations of methods, apparatus (systemsand/or devices), and/or computer program products. It is understood thata block of the block diagrams and/or flowchart illustrations, andcombinations of blocks in the block diagrams and/or flowchartillustrations, can be implemented by computer program instructions.These computer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, and/or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer and/orother programmable data processing apparatus, create means(functionality) and/or structure for implementing the functions/actsspecified in the block diagrams and/or flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instructions whichimplement the functions/acts specified in the block diagrams and/orflowchart block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process, such that theinstructions, which execute on the computer or other programmableapparatus, provide steps for implementing the functions/acts specifiedin the block diagrams and/or flowchart block or blocks.

Accordingly, exemplary embodiments may be implemented in hardware and/orin software (including firmware, resident software, micro-code, etc.).Furthermore, exemplary embodiments may take the form of a computerprogram product on a non-transitory computer-usable or computer-readablestorage medium having computer-usable or computer-readable program codeembodied in the medium for use by or in connection with an instructionexecution system. In the context of this document, a non-transitorycomputer-usable or computer-readable medium may be any medium that cancontain, store, or transport the program for use by or in connectionwith the instruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device. More specificexamples (a non-exhaustive list) of the computer-readable medium wouldinclude the following: a portable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), and a portable compact discread-only memory (CD-ROM).

Computer program code for carrying out operations of data processingsystems discussed herein may be written in a high-level programminglanguage, such as C, C++, or Java, for development convenience. Inaddition, computer program code for carrying out operations of exemplaryembodiments may also be written in other programming languages, such as,but not limited to, interpreted languages. Some modules or routines maybe written in assembly language or even micro-code to enhanceperformance and/or memory usage. However, embodiments are not limited toa particular programming language. It will be further appreciated thatthe functionality of any or all of the program modules may also beimplemented using discrete hardware components, one or more applicationspecific integrated circuits (ASICs), or a programmed digital signalprocessor or microcontroller.

It should also be noted that in some alternate implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved. Moreover, the functionality of a given block of the flowchartsand/or block diagrams may be separated into multiple blocks and/or thefunctionality of two or more blocks of the flowcharts and/or blockdiagrams may be at least partially integrated.

Reference is made to FIGS. 1 a-1 d, which are block diagramsillustrating exemplary networks in which operations for monitoring andreporting network application performance may be performed according tosome embodiments of the present invention.

Computing Network

Referring to FIG. 1 a, a network 10 according to some embodiments hereinmay include a health data processing application 100 and a plurality ofnetwork devices 20, 24, and 26 that may each include respectivecollector applications 200. It is to be understood that a “networkdevice” as discussed herein may include physical (as opposed to virtual)machines 20; host machines 24, each of which may be a physical machineon which one or more virtual machines may execute; and/or virtualmachines 26 executing on host machines 24. It is to be furtherunderstood that an “application” as discussed herein refers to aninstance of executable software operable to execute on respective onesof the network devices. The terms “application” and “networkapplication” may be used interchangeably herein, regardless of whetherthe referenced application is operable to access network resources.

Collector applications 200 may collect data related to the performanceof network applications executing on respective network devices. Forinstance, a collector application executing on a physical machine maycollect performance data related to network applications executing onthat physical machine. A collector application executing on a hostmachine and external to any virtual machines hosted by that host machinemay collect performance data related to network applications executingon that host machine, while a collector application executing on avirtual machine may collect performance data related to networkapplications executing within that virtual machine.

The health data processing application 100 may be on a network devicethat exists within the network 10 or on an external device that iscoupled to the network 10. Accordingly, in some embodiments, the networkdevice on which the health data processing application 100 may residemay be one of the plurality of machines 20 or 24 or virtual machines 26.Communications between various ones of the network devices may beaccomplished using one or more communications and/or network protocolsthat may provide a set of standard rules for data representation,signaling, authentication and/or error detection that may be used tosend information over communications channels therebetween. In someembodiments, exemplary network protocols may include HTTP, TDS, and/orLDAP, among others.

Referring to FIG. 1 b, an exemplary network 10 may include a web server12, one or more application servers 14 and one or more database servers16. Although not illustrated, a network 10 as used herein may includedirectory servers, security servers, and/or transaction monitors, amongothers. The web server 12 may be a computer and/or a computer programthat is responsible for accepting HTTP requests from clients 18 (e.g.,user agents such as web browsers) and serving them HTTP responses alongwith optional data content, which may be, for example, web pages such asHTML documents and linked objects (images, etc.). An application server14 may include a service, hardware, and/or software framework that maybe operable to provide one or more programming applications to clientsin a network. Application servers 14 may be coupled to one or more webservers 12, database servers 16, and/or other application servers 14,among others. Some embodiments provide that a database server 16 mayinclude a computer and/or a computer program that provides databaseservices to other computer programs and/or computers as may be defined,for example by a client-server model, among others. In some embodiments,database management systems may provide database server functionality.

Some embodiments provide that the collector applications 200 and thehealth data processing application 100 described above with respect toFIG. 1 a may reside on ones of the web server(s) 12, application servers14 and/or database servers 16, among others. In some embodiments, thehealth data processing application 100 may reside in a dedicatedcomputing device that is coupled to the network 10. The collectorapplications 200 may reside on one, some or all of the above listednetwork devices and provide network application performance data to thehealth data processing application 100.

Computing Device

Web server(s) 12, application servers 14 and/or database servers 16 maybe deployed as and/or executed on any type and form of computing device,such as a computer, network device, or appliance capable ofcommunicating on any type and form of network and performing theoperations described herein. FIGS. 1 c and 1 d depict block diagrams ofa computing device 121 useful for practicing some embodiments describedherein. Referring to FIGS. 1 c and 1 d, a computing device 121 mayinclude a central processing unit 101 and a main memory unit 122. Acomputing device 100 may include a visual display device 124, a keyboard126, and/or a pointing device 127, such as a mouse. Each computingdevice 121 may also include additional optional elements, such as one ormore input/output devices 130 a-130 b (generally referred to usingreference numeral 130), and a cache memory 140 in communication with thecentral processing unit 101.

The central processing unit 101 is any logic circuitry that responds toand processes instructions fetched from the main memory unit 122. Inmany embodiments, the central processing unit 101 is provided by amicroprocessor unit, such as: those manufactured by Intel Corporation ofMountain View, Calif.; those manufactured by Motorola Corporation ofSchaumburg, Ill.; the POWER processor, those manufactured byInternational Business Machines of White Plains, N.Y.; and/or thosemanufactured by Advanced Micro Devices of Sunnyvale, Calif. Thecomputing device 121 may be based on any of these processors, and/or anyother processor capable of operating as described herein.

Main memory unit 122 may be one or more memory chips capable of storingdata and allowing any storage location to be directly accessed by themicroprocessor 101, such as Static random access memory (SRAM), BurstSRAM or SynchBurst SRAM (BSRAM), Dynamic random access memory (DRAM),Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended DataOutput RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), BurstExtended Data Output DRAM (BEDO DRAM), Enhanced DRAM (EDRAM),synchronous DRAM (SDRAM), JEDEC SRAM, PC100 SDRAM, Double Data RateSDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM),Direct Rambus DRAM (DRDRAM), or Ferroelectric RAM (FRAM), among others.The main memory 122 may be based on any of the above described memorychips, or any other available memory chips capable of operating asdescribed herein. In some embodiments, the processor 101 communicateswith main memory 122 via a system bus 150 (described in more detailbelow). In some embodiments of a computing device 121, the processor 101may communicate directly with main memory 122 via a memory port 103.Some embodiments provide that the main memory 122 may be DRDRAM.

FIG. 1 d depicts some embodiments in which the main processor 101communicates directly with cache memory 140 via a secondary bus,sometimes referred to as a backside bus. In some other embodiments, themain processor 101 may communicate with cache memory 140 using thesystem bus 150. Cache memory 140 typically has a faster response timethan main memory 122 and may be typically provided by SRAM, BSRAM, orEDRAM. In some embodiments, the processor 101 communicates with variousI/O devices 130 via a local system bus 150. Various busses may be usedto connect the central processing unit 101 to any of the I/O devices130, including a VESA VL bus, an ISA bus, an EISA bus, a MicroChannelArchitecture (MCA) bus, a PCI bus, a PCI-X bus, a PCI-Express bus,and/or a NuBus, among others. For embodiments in which the I/O device isa video display 124, the processor 101 may use an Advanced Graphics Port(AGP) to communicate with the display 124. FIG. 1 d depicts someembodiments of a computer 100 in which the main processor 101communicates directly with I/O device 130 via HyperTransport, Rapid I/O,or InfiniBand. FIG. 1 d also depicts some embodiments in which localbusses and direct communication are mixed: the processor 101communicates with I/O device 130 a using a local interconnect bus whilecommunicating with I/O device 130 b directly.

The computing device 121 may support any suitable installation device116, such as a floppy disk drive for receiving floppy disks such as3.5-inch, 5.25-inch disks, or ZIP disks, a CD-ROM drive, a CD-R/RWdrive, a DVD-ROM drive, tape drives of various formats, USB device, harddisk drive (HDD), solid-state drive (SSD), or any other device suitablefor installing software and programs such as any client agent 120, orportion thereof. The computing device 121 may further comprise a storagedevice 128, such as one or more hard disk drives or solid-state drivesor redundant arrays of independent disks, for storing an operatingsystem and other related software, and for storing application softwareprograms such as any program related to the client agent 120.Optionally, any of the installation devices 116 could also be used asthe storage device 128. Additionally, the operating system and thesoftware can be run from a bootable medium, for example, a bootable CD,such as KNOPPIX®, a bootable CD for GNU/Linux that is available as aGNU/Linux distribution from knoppix.net.

Furthermore, the computing device 121 may include a network interface118 to interface to a Local Area Network (LAN), Wide Area Network (WAN)or the Internet through a variety of connections including, but notlimited to, standard telephone lines, LAN or WAN links (e.g., T1, T3, 56kb, X.25), broadband connections (e.g., ISDN, Frame Relay, ATM),wireless connections (e.g., IEEE 802.11), or some combination of any orall of the above. The network interface 118 may comprise a built-innetwork adapter, network interface card, PCMCIA network card, card busnetwork adapter, wireless network adapter, USB network adapter, modem,or any other device suitable for interfacing the computing device 121 toany type of network capable of communication and performing theoperations described herein. A wide variety of I/O devices 130 a-130 nmay be present in the computing device 121. Input devices includekeyboards, mice, trackpads, trackballs, microphones, and drawingtablets, among others. Output devices include video displays, speakers,inkjet printers, laser printers, and dye-sublimation printers, amongothers. The I/O devices 130 may be controlled by an I/O controller 123as shown in FIG. 1 c. The I/O controller may control one or more I/Odevices such as a keyboard 126 and a pointing device 127, e.g., a mouseor optical pen. Furthermore, an I/O device may also provide storage 128and/or an installation medium 116 for the computing device 100. In stillother embodiments, the computing device 121 may provide USB connectionsto receive handheld USB storage devices such USB flash drives.

In some embodiments, the computing device 121 may comprise or beconnected to multiple display devices 124 a-124 n, which each may be ofthe same or different type and/or form. As such, any of the I/O devices130 a-130 n and/or the I/O controller 123 may comprise any type and/orform of suitable hardware, software, or combination of hardware andsoftware to support, enable, or provide for the connection and use ofmultiple display devices 124 a-124 n by the computing device 121. Forexample, the computing device 121 may include any type and/or form ofvideo adapter, video card, driver, and/or library to interface,communicate, connect or otherwise use the display devices 124 a-124 n.In some embodiments, a video adapter may comprise multiple connectors tointerface to multiple display devices 124 a-124 n. In some otherembodiments, the computing device 121 may include multiple videoadapters, with each video adapter connected to one or more of thedisplay devices 124 a-124 n. In some embodiments, any portion of theoperating system of the computing device 100 may be configured for usingmultiple displays 124 a-124 n. In some embodiments, one or more of thedisplay devices 124 a-124 n may be provided by one or more othercomputing devices connected to the computing device 121, for example,via a network. Such embodiments may include any type of softwaredesigned and constructed to use another computer's display device as asecond display device 124 a for the computing device 121. One ordinarilyskilled in the art will recognize and appreciate the various ways andembodiments that a computing device 121 may be configured to havemultiple display devices 124 a-124 n.

In further embodiments, an I/O device 130 may be a bridge 170 betweenthe system bus 150 and an external communication bus, such as a USB bus,an Apple Desktop Bus, an RS-232 serial connection, a SCSI bus, aFireWire bus, a FireWire 800 bus, an Ethernet bus, an AppleTalk bus, aGigabit Ethernet bus, an Asynchronous Transfer Mode bus, a HIPPI bus, aSuper HIPPI bus, a SerialPlus bus, a SCI/LAMP bus, a FibreChannel bus,and/or a Serial Attached small computer system interface bus, amongothers.

A computing device 121 of the sort depicted in FIGS. 1 c and 1 d maytypically operate under the control of operating systems, which controlscheduling of tasks and access to system resources. The computing device121 can be running any operating system such as any of the versions ofthe Microsoft® Windows operating systems, any of the different releasesof the Unix and Linux operating systems, any version of the Mac OS® forMacintosh computers, any embedded operating system, any real-timeoperating system, any open source operating system, any proprietaryoperating system, any operating systems for mobile computing devices,and/or any other operating system capable of running on a computingdevice and performing the operations described herein. Typical operatingsystems include: WINDOWS 3.x, WINDOWS 95, WINDOWS 98, WINDOWS 2000,WINDOWS NT 3.51, WINDOWS NT 4.0, WINDOWS CE, WINDOWS XP, WINDOWS VISTA,WINDOWS 7.0, WINDOWS SERVER 2003, and/or WINDOWS SERVER 2008, all ofwhich are manufactured by Microsoft Corporation of Redmond, Wash.;MacOS, manufactured by Apple Computer of Cupertino, Calif.; OS/2,manufactured by International Business Machines of Armonk, N.Y.; andLinux, a freely-available operating system distributed by Red Hat ofRaleigh, N.C., among others, or any type and/or form of a Unix operatingsystem, among others.

In some embodiments, the computing device 121 may have differentprocessors, operating systems, and input devices consistent with thedevice. For example, in one embodiment the computing device 121 is aTreo 180, 270, 1060, 600 or 650 smart phone manufactured by Palm, Inc.In this embodiment, the Treo smart phone is operated under the controlof the PalmOS operating system and includes a stylus input device aswell as a five-way navigator device. Moreover, the computing device 121can be any workstation, desktop computer, laptop, or notebook computer,server, handheld computer, mobile telephone, any other computer, orother form of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein.

Architecture

Reference is now made to FIG. 2, which is a block diagram illustratingan architecture of a computing device 121 as discussed above regardingFIGS. 1 c and 1 d. The architecture of the computing device 121 isprovided by way of illustration only and is not intended to be limiting.The architecture of computing device 121 may include a hardware layer206 and a software layer divided into a user space 202 and a kernelspace 204.

Hardware layer 206 may provide the hardware elements upon which programsand services within kernel space 204 and user space 202 are executed.Hardware layer 206 also provides the structures and elements that allowprograms and services within kernel space 204 and user space 202 tocommunicate data both internally and externally with respect tocomputing device 121. The hardware layer 206 may include a processingunit 262 for executing software programs and services, a memory 264 forstoring software and data, and network ports 266 for transmitting andreceiving data over a network. Additionally, the hardware layer 206 mayinclude multiple processors for the processing unit 262. For example, insome embodiments, the computing device 121 may include a first processor262 and a second processor 262′. In some embodiments, the processor 262or 262′ includes a multi-core processor. The processor 262 may includeany of the processors 101 described above in connection with FIGS. 1 cand 1 d.

Although the hardware layer 206 of computing device 121 is illustratedwith certain elements in FIG. 2, the hardware portions or components ofcomputing device 121 may include any type and form of elements, hardwareor software, of a computing device, such as the computing device 121illustrated and discussed herein in conjunction with FIGS. 1 c and 1 d.In some embodiments, the computing device 121 may comprise a server,gateway, router, switch, bridge, or other type of computing or networkdevice, and have any hardware and/or software elements associatedtherewith.

The operating system of computing device 121 allocates, manages, orotherwise segregates the available system memory into kernel space 204and user space 202. As discussed above, in the exemplary softwarearchitecture, the operating system may be any type and/or form ofvarious ones of different operating systems capable of running on thecomputing device 121 and performing the operations described herein.

The kernel space 204 may be reserved for running the kernel 230,including any device drivers, kernel extensions, and/or other kernelrelated software. As known to those skilled in the art, the kernel 230is the core of the operating system, and provides access, control, andmanagement of resources and hardware-related elements of theapplications. In accordance with some embodiments of the computingdevice 121, the kernel space 204 also includes a number of networkservices or processes working in conjunction with a cache managersometimes also referred to as the integrated cache. Additionally, someembodiments of the kernel 230 will depend on embodiments of theoperating system installed, configured, or otherwise used by the device121.

In some embodiments, the device 121 includes one network stack 267, suchas a TCP/IP based stack, for communicating with a client and/or aserver. In other embodiments, the device 121 may include multiplenetwork stacks. In some embodiments, the network stack 267 includes abuffer 243 for queuing one or more network packets for transmission bythe computing device 121.

As shown in FIG. 2, the kernel space 204 includes a high-speed layer 2-7integrated packet engine 240 and a policy engine 236. Running packetengine 240 and/or policy engine 236 in kernel space 204 or kernel modeinstead of the user space 202 improves the performance of each of thesecomponents, alone and in combination. Kernel operation means that packetengine 240 and/or policy engine 236 run in the core address space of theoperating system of the device 121. For example, data obtained in kernelmode may not need to be passed or copied to a process or thread runningin user mode, such as from a kernel level data structure to a user leveldata structure. In this regard, such data may be difficult to determinefor purposes of network application performance monitoring. In anotheraspect, the number of context switches between kernel mode and user modeare also reduced. Additionally, synchronization of and communicationsbetween packet engine 240 and/or policy engine 236 can be performed moreefficiently in the kernel space 204.

In some embodiments, any portion of the packet engine 240 and/or policyengine 236 may run or operate in the kernel space 204, while otherportions of packet engine 240 and/or policy engine 236 may run oroperate in user space 202. In some embodiments, the computing device 121uses a kernel-level data structure providing access to any portion ofone or more network packets, for example, a network packet comprising arequest from a client or a response from a server. In some embodiments,the kernel-level data structure may be obtained by the packet engine 240via a transport layer driver interface (TDI) or filter to the networkstack 267. The kernel-level data structure may include any interfaceand/or data accessible via the kernel space 204 related to the networkstack 267, network traffic, or packets received or transmitted by thenetwork stack 267. In some embodiments, the kernel-level data structuremay be used by packet engine 240 and/or policy engine 236 to perform thedesired operation of the component or process. Some embodiments providethat packet engine 240 and/or policy engine 236 is running in kernelmode 204 when using the kernel-level data structure, while in some otherembodiments, the packet engine 240 and/or policy engine 236 is runningin user mode when using the kernel-level data structure. In someembodiments, the kernel-level data structure may be copied or passed toa second kernel-level data structure, or any desired user-level datastructure.

A policy engine 236 may include, for example, an intelligent statisticalengine or other programmable application(s). In some embodiments, thepolicy engine 236 provides a configuration mechanism to allow a user toidentify, specify, define or configure a caching policy. Policy engine236, in some embodiments, also has access to memory to support datastructures such as lookup tables or hash tables to enable user-selectedcaching policy decisions. In some embodiments, the policy engine 236 mayinclude any logic, rules, functions or operations to determine andprovide access, control and management of objects, data or content beingcached by the computing device 121 in addition to access, control andmanagement of security, network traffic, network access, compression,and/or any other function or operation performed by the computing device121.

High speed layer 2-7 integrated packet engine 240, also generallyreferred to as a packet processing engine or packet engine, isresponsible for managing the kernel-level processing of packets receivedand transmitted by computing device 121 via network ports 266. The highspeed layer 2-7 integrated packet engine 240 may include a buffer forqueuing one or more network packets during processing, such as forreceipt of a network packet or transmission of a network packer.Additionally, the high speed layer 2-7 integrated packet engine 240 isin communication with one or more network stacks 267 to send and receivenetwork packets via network ports 266. The high speed layer 2-7integrated packet engine 240 may work in conjunction with policy engine236. In particular, policy engine 236 is configured to perform functionsrelated to traffic management such as request-level content switchingand request-level cache redirection.

The high speed layer 2-7 integrated packet engine 240 includes a packetprocessing timer 242. In some embodiments, the packet processing timer242 provides one or more time intervals to trigger the processing ofincoming (i.e., received) or outgoing (i.e., transmitted) networkpackets. In some embodiments, the high speed layer 2-7 integrated packetengine 240 processes network packets responsive to the timer 242. Thepacket processing timer 242 provides any type and form of signal to thepacket engine 240 to notify, trigger, or communicate a time relatedevent, interval or occurrence. In many embodiments, the packetprocessing timer 242 operates in the order of milliseconds, such as forexample 100 ms, 50 ms, or ms. For example, in some embodiments, thepacket processing timer 242 provides time intervals or otherwise causesa network packet to be processed by the high speed layer 2-7 integratedpacket engine 240 at a 10 ms time interval, while in other embodiments,at a 5 ms time interval, and still yet in further embodiments, as shortas a 3, 2, or 1 ms time interval. The high speed layer 2-7 integratedpacket engine 240 may be interfaced, integrated and/or in communicationwith the policy engine 236 during operation. As such, any of the logic,functions, or operations of the policy engine 236 may be performedresponsive to the packet processing timer 242 and/or the packet engine240. Therefore, any of the logic, functions, and/or operations of thepolicy engine 236 may be performed at the granularity of time intervalsprovided via the packet processing timer 242, for example, at a timeinterval of less than or equal to 10 ms.

In contrast to kernel space 204, user space 202 is the memory area orportion of the operating system used by user mode applications orprograms otherwise running in user mode. Generally, a user modeapplication may not access kernel space 204 directly, and instead mustuse service calls in order to access kernel services. As shown in FIG.2, user space 202 of computing device 121 includes a graphical userinterface (GUI) 210, a command line interface (CLI) 212, shell services214, and daemon services 218. Using GUI 210 and/or CLI 212, a systemadministrator or other user may interact with and control the operationof computing device 121. The GUI 210 may be any type and form ofgraphical user interface and may be presented via text, graphical orotherwise, by any type of program or application, such as a browser. TheCLI 212 may be any type and form of command line or text-basedinterface, such as a command line provided by the operating system. Forexample, the CLI 212 may comprise a shell, which is a tool to enableusers to interact with the operating system. In some embodiments, theCLI 212 may be provided via a bash, csh, tcsh, and/or ksh type shell.The shell services 214 may include the programs, services, tasks,processes and/or executable instructions to support interaction with thecomputing device 121 or operating system by a user via the GUI 210and/or CLI 212.

Daemon services 218 are programs that run continuously or in thebackground and handle periodic service requests received by computingdevice 121. In some embodiments, a daemon service may forward therequests to other programs or processes, such as another daemon service218 as appropriate. As known to those skilled in the art, a daemonservice 218 may run unattended to perform continuous and/or periodicsystem wide functions, such as network control, or to perform anydesired task. In some embodiments, one or more daemon services 218 runin the user space 202, while in other embodiments, one or more daemonservices 218 run in the kernel space.

Collector Application

Reference is now made to FIG. 3, which is a block diagram illustratingoperations and/or functions of a collector application 200 as describedabove regarding FIG. 1 a. The collector application 200 includes akernel space module 310 and a user space module 320. The kernel spacemodule 310 may generally operate to intercept network activities as theyoccur. Some embodiments provide that the kernel space module 310 may usea kernel mode interface in the operating system, such as, for example,Microsoft Windows transport data interface (TDI). The kernel spacemodule 310 may include a TDI filter 314 that is configured to monitorand/or intercept interactions between applications. Additionally, someembodiments provide that the kernel space module 310 may include anancillary functions driver (AFD) filter 312 that is configured tointercept read operations and the time of their duration. Some operatingsystems may include a kernel mode driver other than the AFD. In thisregard, operations described herein may be used with other such kernelmode drivers to intercept application operational data.

The raw data related to the occurrence of and attributes of transactionsbetween network applications may be generally referred to as“performance data.” The raw data may have value for diagnosing networkapplication performance issues and/or for identifying and understandingthe structure of the network applications. The measurements oraggregations of performance data may be generally referred to as“metrics” or “performance metrics.” Performance data and the metricsgenerated therefrom may be temporally relevant—i.e., the performancedata and the metrics may be directly related to and/or indicative of thehealth of the network at the time the performance data is collected.Performance data may be collected, and metrics based thereon may begenerated, on a client side and/or a server side of an interaction. Someembodiments provide that performance data is collected in substantiallyreal-time. In this context, “substantially real-time” means thatperformance data is collected immediately subsequent to the occurrenceof the related network activity, subject to the delays inherent in theoperation of the computing device and/or the network and in the methodof collection. The performance data collected and/or the metricsgenerated may correspond to a predefined time interval. For example, atime interval may be defined according to the dynamics of the networkand may include exemplary period lengths of less than 1, 1, 5, 10, 15,20, 30, and/or 60, seconds, among others.

Exemplary client side metrics may be aggregated according to one or moreapplications or processes. For example, the client side metrics may beaggregated according to destination address, port number, and a localprocess identifier (PID). A PID may be a number used by some operatingsystem kernels to uniquely identify a process. This number may be usedas a parameter in various function calls allowing processes to bemanipulated, such as adjusting the process's priority and/or terminatingthe process. In this manner, multiple connections from the sameapplication or process to the same remote service may be aggregated. Asdiscussed in more detail with respect to FIGS. 10-11, client sidemetrics for processes that work together as a single logical unit mayalso be aggregated into process pools.

Similarly, server side metrics may be aggregated according to the sameapplication or service regardless of the client. For example, someembodiments provide that server side metrics may be aggregated accordingto local address, port number, and PID. Respective ones of the clientside and server side metrics may be collected from the kernel spaceand/or user space.

The kernel space module 310 may include a kernel events sender 316 thatis configured to receive performance data from the AFD filter 312 and/orthe TDI filter 314, and generate metrics based on the performance datafor receipt by a kernel events receiver 322 in the user space module320. In the user space module 320, metrics data received by the kernelevent receiver 322 may be processed by a reverse domain name system(DNS) resolver 325 to map an observed network address to a moreuser-friendly DNS name. Additionally, metrics data received by thekernel events receiver 322 may be used by a process resolver 326 todetermine the processes and/or applications corresponding to thecollected kernel metrics data.

The user space module 320 may include a machine information collector324 that is operable to determine static machine information, such as,for example, CPU speed, memory capacity, and/or operating systemversion, among others. As the performance data is collectedcorresponding to applications and/or processes, the machine informationmay be non-correlative relative to the applications and/or processes.The user space module 320 may include a process data collector 328 thatcollects data corresponding to the processes and/or applicationsdetermined in the process resolver 326. A machine performance datacollector 330 may collect machine specific performance data. Examples ofmachine data may include information about resource utilization such asthe amount of memory in use and/or the percentage of available CPU timeconsumed. The user space module 320 may include an event dispatcher 332that is configured to receive the machine information, resolved DNSinformation, process identification, process data, and/or machine data,and to generate events incorporating the aggregated metrics data fordispatch to a health data processor application 100 that is operable toreceive aggregated metrics data from multiple collectors 200.

Some embodiments provide that the performance data collected and/ormetrics generated may be diagnostically equivalent and, thus, may beaggregated into a single event. The identification process may depend onwhich application initiates a network connection and which end of theconnection is represented by a current collector application host.

Kernel level metrics may generally include data corresponding to readoperations that are in progress. For example, reference is now made toFIG. 4, which is a diagram illustrating determining a read wait timecorresponding to a user transaction according to some embodiments of thepresent invention. A user transaction between a client 401 and a server402 are initiated when the client 401 sends a write request at time T1to the server 402. The server 402 completes reading the request at timeT2 and responds to the request at time T3 and the client 401 receivesthe response from the server 402 at time T4. A kernel metric that may bedetermined is the amount of time spent between beginning a readoperation and completing the read operation. In this regard, clientmeasured server response time 410 is the elapsed time between when therequest is sent (T1) and when a response to the request is read (T4) bythe client. Accordingly, the client measured server response time 410may be determined as T4−T1. The server 402 may determine a servermeasured server response time 412 that is the elapsed time between whenthe request is read (T2) by the server 402 and when the response to therequest is sent (T3) by the server 402 to the client 401. Accordingly,the server measured server response time 412 may be determined as T3−T2.

As the application response is measured in terms of inbound and outboundpackets, the application response time may be determined in anapplication agnostic manner.

Additionally, another metric that may be determined is the read waittime 414, which is the elapsed time between when the client 401 is readyto read a response to the request T5 and when the response to therequest is actually read T4. In some embodiments, the read wait time mayrepresent a portion of the client measured server response time 410 thatmay be improved upon by improving performance of the server 402.Further, the difference between the client measured server response time410 and the server measured server response time 412 may be used todetermine the total transmission time of the data between the client 401and the server 402. Some embodiments provide that the values may not bedetermined until a read completes. In this regard, pending reads may notbe included in this metric. Further, as a practical matter, higherand/or increasing read time metrics discussed above may be indicative ofa slow and/or poor performing server 402 and/or protocol where at leastsome messages originate unsolicited at the server 402.

Other read metrics that may be determined include the number of pendingreads. For example, the number of read operations that have begun butare not yet completed may be used to detect high concurrency. In thisregard, high and/or increasing numbers of pending read operations mayindicate that a server 402 is not keeping up with the workload. Someembodiments provide that the total number of reads may include readsthat began at a time before the most recent aggregated time period.

Additionally, some embodiments provide that the number of reads thatwere completed during the last time period may be determined. An averageof read wait time per read may be generated by dividing the total readwait time, corresponding to a sum of all of the T4−T5 values during thetime period, by the number of completed reads in that period.

In some embodiments, the number of stalled reads may be determined asthe number of pending reads that began earlier than a predefinedthreshold. For example, a predefined threshold of 60 seconds may providethat the number of pending read operations that began more than 60seconds ago are identified as stalled read operations. Typically, anyvalue greater than zero may be undesirable and/or may be indicative of aserver-initiated protocol. Some embodiments may also determine thenumber of bytes sent/received on a connection.

The number of completed responses may be estimated as the number oftimes a client-to-server message (commonly interpreted as a request) wasfollowed by a server-to-client message (commonly interpreted as aresponse). Some embodiments provide that this may be measured by boththe server and the client connections. In some embodiments, this may bethe same as the number of completed reads for a given connection.Additionally, a total response time may be estimated as the total timespent in request-to-response pairs.

Reference is now made to FIG. 5, which is a block diagram illustrating akernel level architecture of a collector application 200 to explainkernel level metrics according to some embodiments of the presentinvention. As discussed above, regarding FIG. 3, the collector may use aTDI filter 314 and an AFD filter 312. The AFD filter 312 may interceptnetwork activity from user space processes that use a library defined ina standard interface between a client application and an underlyingprotocol stack in the kernel.

The TDI filter 314 may operate on a lower layer of the kernel and canintercept all network activity. As the amount of information availableat AFD filter 312 and TDI filter 314 is different, the performance datathat may be collected and the metrics that may be generated using eachmay also be different. For example, the AFD filter 312 may collect AFDperformance data and generate AFD metrics that include total read waittime, number of completed reads, number of pending reads and number ofstalled reads, among others. The TDI filter may collect TDI performancedata and generate TDI metrics including total bytes sent, total bytesreceived, total response time and the number of responses from theserver. Depending on the architecture of a target application, the AFDmetrics for client-side connections may or may not be available. In thisregard, if the application uses the standard interface, the collectormay report non-zero AFD metrics. Otherwise, all AFD metrics may not bereported or may be reported as zero.

Some embodiments provide that kernel level metrics may be generatedcorresponding to specific events. Events may include read wait metricsthat may include client side metrics such as total read wait time,number of completed reads, number of pending reads, number of stalledreads, bytes sent, bytes received, total response time, and/or number ofresponses, among others. Events may further include server responsemetrics such as bytes sent, bytes received, total response time and/ornumber of responses, among others.

In addition to the kernel metrics discussed above, the collector 200 mayalso generate user level metrics. Such user level metrics may include,but are not limited to aggregate CPU percentage (representing thepercentage of CPU time across all cores), aggregate memory percentage(i.e., the percentage of physical memory in use by a process and/or allprocesses), and/or total network bytes sent/received on all networkinterfaces, among others. User level metrics may include, but are notlimited to, the number of page faults (the number of times any processtries to read from or write to a page that was not in its resident inmemory), the number of pages input (i.e., the number of times anyprocess tried to read a page that had to be read from disk), and/or thenumber of pages output (representing the number of pages that wereevicted by the operating system memory manager because it was low onphysical memory), among others. User level metrics may include, but arenot limited to, a queue length (the number of outstanding read or writerequests at the time the metric was requested), the number of bytes readfrom and/or written to a logical disk in the last time period, thenumber of completed read/write requests on a logical disk in the lasttime period, and/or total read/write wait times (corresponding to thenumber of milliseconds spent waiting for read/write requests on alogical disk in the last time interval), among others.

Further, some additional metrics may be generated using data fromexternal application programming interfaces. Such metrics may include,for example: the amount of memory currently in use by a machine memorycontrol driver; CPU usage expressed as a percentage; memory currentlyused as a percentage of total memory; and/or total network bytessent/received, among others.

In some embodiments, events may be generated responsive to certainoccurrences in the network. For example events may be generated: when aconnection, such as a TCP connection, is established from or to amachine; when a connection was established in the past and the collectorapplication 200 first connects to the health data processing application100; and/or when a connection originating from the current machine wasattempted but failed due to timeout, refusal, or because the network wasunreachable. Events may be generated when a connection is terminated;when a local server process is listening on a port; when a local serverprocess began listening on a port in the past and the collectorapplication 200 first connects to the health data processing application100; and/or when a local server process ceases to listen on a port.Events may be generated if local network interfaces have changed and/orif a known type of event occurs but some fields are unknown. Events mayinclude a description of the static properties of a machine when acollector application 200 first connects to a health data processingapplication 100; process information data when a process generates itsfirst network-related event; and/or information about physical disks andlogical disks when a collector application 200 first connects to ahealth data processing application 100.

Some embodiments provide that the different link events may includedifferent data types corresponding to the type of information relatedthereto. For example, data strings may be used for a type description ofan event. Other types of data may include integer, bytes and/or Boolean,among others.

In some embodiments, the events generated by collector application 200for dispatch to heath data processing application 100 may incorporatemetrics related to network structure, network health, computationalresource health, virtual machine structure, virtual machine health,and/or process identification, among others. Metrics related to networkstructure may include data identifying the network device on whichcollector application 200 is executing, or data related to theexistence, establishment, or termination of network links, or theexistence of bound ports or the binding or unbinding of ports. Metricspertinent to network health may include data related to pending,completed, and stalled reads, bytes transferred, and response times,from the perspective of the client and/or the server side. Metricsrelated to computational resource health may include data regarding theperformance of the network device on which collector application 200 isexecuting, such as processing and memory usage. Metrics related tovirtual machine structure may include data identifying the physical hostmachine on which collector application 200 is executing, and/or dataidentifying the virtual machines executing on the physical host machine.Metrics pertinent to virtual machine health may include regarding theperformance of the host machine and/or the virtual machines executing onthe host machine, such as processing and memory usage as determined fromthe perspective of the host machine and/or the virtual machines.Finally, metrics related to process identification may include dataidentifying individual processes executing on a network device.

Reference is made to FIG. 6, which illustrates exemplary operations thatmay be carried out by collector application 200 in monitoring andreporting network application performance according to some embodimentsof the present invention. At block 600, collector application 200establishes hooks on a networked device to an internal network protocolkernel interface utilized by the operating system of the networkeddevice. In some embodiments, these hooks may include, for instance, aTDI filter. Collector application 200 also establishes hooks to anapplication oriented system call interface to a transport network stack.The hooks may include, in some embodiments, an AFD filter. Collectorapplication 200 collects, via the established hooks, performance datacorresponding to at least one network application running on thenetworked device (block 602). At block 604, kernel level and user levelmetrics are generated based on the collected performance data. Thegenerated metrics may provide an indication of the occurrence of aninteraction (e.g., establishment of a network link), or may providemeasurements of, for instance, a count of some attribute of thecollected performance data (e.g., number of completed reads) or asummation of some attribute of the collected performance data (e.g.,total read attempts). The kernel level and user level metrics areaggregated by application—e.g., by aggregating metrics associated withthe same IP address, local port, and process ID (block 606). At block608, the kernel level and user level metrics generated within aspecified time interval are aggregated. For instance, in someembodiments, metrics generated within the most recent 15-second timeinterval are aggregated.

At block 610, redundant data is removed from the aggregated metrics, andinconsistent data therein is reconciled. Redundant data may include, forinstance, functionally equivalent data received from both the TDI andAFD filters. Collector application 200 performs a reverse DNS lookup todetermine the DNS name associated with IP addresses referenced in thegenerated kernel level and user level metrics (block 612). Finally, atblock 614, an event is generated, incorporating the kernel level anduser level metrics and the determined DNS name(s). The generated eventmay be subsequently transmitted to health data processing application100 for incorporation into a model of network health status.

Installation without Interruption

In some embodiments, the collector application 200 may be installed intoa machine of interest without requiring a reboot of the machine. Thismay be particularly useful in the context of a continuously operablesystem, process and/or operation as may be frequently found inmanufacturing environments, among others. As the collector operationsinterface with the kernel, and more specifically, the protocol stack,installation without rebooting may entail intercepting requests comingin and out of the kernel using the TDI filter. Some embodiments includedetermining dynamically critical offsets in potentially undocumenteddata structures. Such offsets may be used in intercepting networkactivity for ports and connections that exist prior to an installationof the collector application 200. For example, such previously existingports and connections may be referred to as the extant state of themachine.

Some embodiments provide that intercepting the stack data may includeoverwriting the existing stack function tables with pointers and/ormemory addresses that redirect the request through the collector filterand then to the intended function. In some embodiments, the existingstack function tables may be overwritten atomically in that theoverwriting may occur at the smallest indivisible data level. Each entryin a function table may generally include a function pointer and acorresponding argument. However, only one of these entries (either thefunction or the argument) can be overwritten at one time. Thus,intercepting function calls may rely on two consecutive overwrites ofthe stack data corresponding to the function and corresponding argument.In some embodiments, there is no means for protecting from anintervening operation between overwriting one of the function andargument and overwriting the other one of them. In this regard, systemstability may be at risk from two attempted consecutive overwrites.

As the consecutive overwrites of intercepting function calls may placethe machine at risk of instability, a dynamic overwriting operation maybe used. Specifically, a separate data structure is provided thatincludes a pointer to the original function, its original argument anddynamically generated code to invoke a filter in the collectorapplication 200. The address of this data structure may be used toatomically overwrite the original function pointer in a singleoperation. The collector collects the data and then calls the originalfunction corresponding to the overwritten stack data to perform itsintended purpose. In this manner, the original behavior of the machineis preserved and the collector application collects the relevant datawithout rebooting the machine and/or placing the machine at risk ofinstability.

Some embodiments may include identifying the potentially undocumenteddata structures representing bound ports and network connections. Forexample, TDI objects (connections and bound ports) created prior to theinstallation of the collector application 200 may be determined by firstenumerating all objects identified in a system. Each of the enumeratedobjects may be tagged with an identifier corresponding to itssub-system. A request corresponding to a known TDI object is created andsent for processing. The type codes of the enumerated objects arecompared to those of the known TDI object to determine which of theobjects are ports and which of the objects are connections. Theenumerated objects may then be filtered as either connections or ports.

In some embodiments, this may be accomplished using an in-kernel thread.The thread may monitor network connections having restricted visibilityand may detect when a monitored connection no longer exists. Connectionsmay be added dynamically to the monitored list as needed.

Some embodiments provide that events may be generated to indicate thatvisibility into network events may be incomplete. For example,information may be missing corresponding to an active process, the stateof a known connection, and/or missing information regarding networkactivity. In this manner, depending on conditions, a custom event can betransmitted to indicate what type of information is missing and whatprocess may be responsible for that information.

Health Data Processing Application

In some embodiments, the health data processing application 100 may beoperable to receive, from at least one collector application 200,network activity data corresponding to network activity of theapplications on the network device on which the collector application200 is installed. The health data processing application 100 may combinethe network activity data received from the collector application 200 toremove redundant portions thereof. In some embodiments, the health dataprocessing application 100 may archive the received activity data in apersistent data store along with a timestamp indicating when theactivity data was collected and/or received. The health data processingapplication 100 may generate a model that includes identified networkapplication components and their relatedness and/or links therebetween.The generated model may be displayed via one or more display devicessuch as, e.g., display devices 124 a-124 n discussed in greater detailabove.

In some embodiments, the health data processing application 100 may beoperable to combine network activity data reported from multiplecollector applications 200 to eliminate redundancy and to addressinconsistencies among data reported by different collector applications200. For example, network data from multiple collector applications 200may be stitched together to create a consistent view of the health ofthe network applications.

Some embodiments provide that the model may be a graphical display ofthe network including application components (machines, clients,processes, etc.) and the relationships therebetween. In someembodiments, the model may be generated as to reflect the real-time ornear-real-time activity of the network. It is to be understood that, inthis context, “near-real-time” may refer to activity occurring in themost recent of a specified time interval for which activity data wasreceived. For instance, health data processing application 100 mayreceive from collector applications 200 aggregated activity datacorresponding to the most recent 15-second interval of networkoperation, and, accordingly, the model of near-real-time activity mayreflect the activity of the network as it existed during that mostrecent 15-second interval.

Some embodiments provide that the model may be generated to reflect anhistorical view of network activity data corresponding to a specifiedtime interval. The historical view may be generated based on archivedactivity data retrieved from a persistent data store and having atimestamp indicating that the activity data was collected or receivedduring the specified time interval. In other embodiments, the model maybe dynamically updated to reflect new and/or lost network collectorsand/or network components. Further, graphs may be provided at eachand/or selected network resource indicators to show activity data overpart of and/or all of the time interval.

In some embodiments, a model may include sparklines to provide quickaccess to trends of important metrics, process and application views toprovide different levels of system detail, and/or model overlays toprovide additional application analysis. For example, visual feedbackregarding the contribution of a network link relative to a givencriterion may be provided. In this manner, hop by hop transaction dataabout the health of applications can be provided. Additionally, visualranking of connections based on that criteria may be provided.Bottleneck analysis based on estimated response times may be provided toidentify slow machines, applications, and/or processes, among others.

Some embodiments provide that health data processing application 100 maybe operable to infer the existence of network devices and/or networkapplications for which no activity data was received or on which nocollector application 200 is running, based on the identification ofother network devices and/or other network applications for whichactivity data was received. For instance, activity data received byhealth data processing application 100 may indicate that a network linkhas been established between a local network device running collectorapplication 200 and a remote network device that is not runningcollector application 200. Because the activity data may includeidentifying information for both the local and remote network devices,health data processing application 100 may infer that the remote networkdevice exists, and incorporate the remote network device into thegenerated model of network activity.

In other embodiments, health data processing application 100 may beoperable to identify a network application based on predefinedtelecommunications standards, such as, e.g., the port numbers listmaintained by the Internet Assigned Numbers Authority (IANA). Healthdata processing application 100 may, for example, receive activity dataindicating that a process on a network device is bound to port 21. Bycross-referencing the indicated port number with the IANA port numberslist, health data processing application 100 may identify the process asan File Transfer Protocol (FTP) server, and may include theidentification in the generated model.

Reference is made to FIG. 7, which is a screen shot of a graphical userinterface (GUI) including a model generated by a health data processingapplication according to some embodiments of the present invention. TheGUI 700 includes a model portion 701 that illustrates representations ofvarious network applications and/or application components 702. Suchrepresentations may include identifier fields 704 that are operable toidentify application and/or application component addresses, ports,machines and/or networks. Connections 706 between network applicationsand/or application components may be operable to convey additionalinformation via color, size and/or other graphical and/or text-basedinformation. A summary field 708 may be provided to illustrate summaryinformation corresponding to one or more applications and/or applicationcomponents, among others. A port identification portion 712 may beoperable to show the connections corresponding to and/or through aparticular port. The GUI 700 may include a system and/or networknavigation field 710, overlay selection field 714, and one or more timeinterval and/or snapshot field(s) 716.

FIG. 8 is a flowchart illustrating exemplary operations that may becarried out by health data processing application 100 in generating anddisplaying a real-time model of network application health according tosome embodiments of the present invention. At block 800, health dataprocessing application 100 may receive activity data from a plurality ofcollector applications 200 executing on respective ones of a pluralityof network devices. The received activity data corresponds to activitiesof a plurality of network applications executing on respective ones ofthe plurality of networked devices. At block 802, the received activitydata is archived along with a timestamp indicating when the activitydata was collected and/or received. As discussed in greater detail withrespect to FIG. 9, this archived data may allow health data processingapplication 100 to generate and display an historical model of networkapplication health during a specified time interval. At block 804, thereceived activity data is combined to remove redundant data and toreconcile inconsistent data. At block 806, health data processingapplication 100 identifies the network applications executing on therespective ones of the plurality of networked devices, and ascertainsthe relationships therebetween. The identification of the networkapplications and the relationships therebetween may be based on thereceived activity data, and may further be determined based on acorrelation between the received activity data and predefined industrystandards, as discussed above. At block 808, health data processingapplication 100 may infer the existence of network applications forwhich no activity data was received, based on the identification ofnetwork applications for which activity data was received. At block 810,a real-time model of network health status, including the identifiednetwork applications and the relationships therebetween, is generated,and the model is displayed at block 812.

FIG. 9 is a flowchart illustrating exemplary operations carried out by ahealth data processing application 100 in generating and displaying anhistorical model of network application health according to someembodiments of the present invention. At block 900, the activity datapreviously archived at block 802 and corresponding to a specified timeinterval is retrieved. The retrieved activity data is combined to removeredundant data and reconcile inconsistent data at block 902. At block904, health data processing application 100 identifies the networkapplications associated with the retrieved activity data, and ascertainsthe relationships therebetween. The identification of the networkapplications and the relationships therebetween may be based on theretrieved activity data, and may further be determined based oncorrelation between the retrieved activity data and industry standards.At block 906, health data processing application 100 may infer theexistence of network applications for which no activity data wasretrieved, based on the identification of network applications for whichactivity data was retrieved. At block 908, an historical model ofnetwork health status in the specified time interval, including theidentified network applications and the relationships therebetween, isgenerated, and the historical model is displayed at block 910.

Custom Protocol

Some embodiments provide that transferring the activity data between thecollector applications 200 and the health data processing application100 may be performed using a compact, self-describing, linear buffercommunications protocol. In some embodiments, the custom protocol uses acommon representation for monitoring information, commands andconfiguration data. As the methods and systems described herein areintended to monitor network performance, the protocol may be operable tominimize the volume of information exchanged between the collectorapplications 200 and the health data processing application 100.

In some embodiments, the collector applications 200 are operable togenerate events in a streaming data format. Events may be generatedcorresponding to the predefined monitoring time period. Informationprovided corresponding to an event may include an event type, networkresource identification data including PID, remote identifiers,quantities and/or types of data sent/received, and/or response timeinformation, among others. The protocol may include a banner portionthat may be established through a handshaking process that may occurwhen a collector application 200 initially communicates with the healthdata processing application 100. The banner portion may define the datatypes and formats to be transferred. In this manner, the protocol may beflexible by virtue of the self-descriptive banner portion and may avoidsending unused, unwanted or blank data fields.

Identifying and Monitoring Related Business Application Processes

Where a network includes a large number of individual hardware and/orsoftware components, a model of network health status generated anddisplayed by health data processing application 100 may prove difficultto comprehend or utilize, due to the complexity of the representationsof the individual hardware and software components and the relationshipstherebetween. Accordingly, in some embodiments, health data processingapplication 100 may provide a simplified representation of businessapplications by identifying multiple processes that are associated witha specified business application, and providing aggregated performancemetrics summarizing the performance data for the multiple processesassociated with the specified business application.

To identify the processes that constitute a business application, healthdata processing application 100 may begin with a representation of knownnetwork processes and devices. In some embodiments, the representationmay be a directed process dependency graph, which may be a dataconstruct indicating the dependency relationships between variousprocesses. In the context of a directed process dependency graph, afirst process that utilizes services provided by a second process issaid to depend on the second process. In some embodiments, therepresentation of known network processes and devices may correspond toor be derived from the model of network health generated by health dataprocessing application 100.

FIG. 10 is a visualization of a simple directed process dependencygraph. Each entity in the graph, such as database server 1000 and webserver 1005, represents a running process, while the arrows connectingthe entities represent the dependency relationship between theprocesses. For instance, the arrow originating at database server 1000and terminating at app server 1045 indicates that app server 1045depends on, or utilizes services provided by, database server 1000.Likewise, business application front end 1030 depends on app server1045. It is to be understood that some processes in the directed processdependency graph may be related in such a way that their dependenciescreate a cycle in which there is no clear “parent” process. For example,as seen in FIG. 10, web server 1005 depends on web server 1010, whichdepends on web server 1015, which in turn depends on web server 1005,creating a cycle.

Health data processing application 100 may identify, in the directedprocess dependency graph, a first process that is known to correspond toa commonly used transactional business application. Transactionalbusiness applications may include, for instance, enterprise databasessuch as Oracle, DB2, or SQL Server; web servers such as InternetInformation Server (IIS) or Apache; and/or application servers such asWebSphere or Weblogic. Processes known to correspond to transactionalbusiness applications may be identified using, e.g., a configurationfile that is external to health data processing application 100.

After the first process is identified, one or more root processes maythen be identified by health data processing application 100 by tracinga dependency path from the first process through the processes dependingon the first process until a root process is reached. A root process maybe, for instance, a process that depends directly and/or indirectly onthe first process, and that has no other processes depending on it.Generally, a root process may correspond to the application componentwith which the user directly interacts, and, thus, may represent the“face” of the business application to the user.

An example of tracing a dependency path may be illustrated by referenceto FIG. 10. Health data processing application 100 may identify databaseserver 1000 as a process corresponding to a known transactional businessapplication. Using the dependency information in the directed processdependency graph, health data processing application 100 may then tracea dependency path from database server 1000 to app server 1045, and thento business application front end 1030. Because business applicationfront end 1030 depends indirectly on database server 1000 and has noother dependencies itself, it may be considered a root process.

In tracing a dependency path, it may be desirable to exclude someprocesses for consideration as a root process. For example, some clientprocesses, such as web browser applications, may not properly beconsidered to be associated with a business application, despite thedependencies indicated in the directed process dependency graph.Accordingly, in some embodiments, known client application processeswhich are not to be associated with business applications may beidentified, e.g., in a configuration file external to health dataprocessing application 100. If a root process being considered by healthdata processing application 100 corresponds to a known clientapplication process, the potential root process is disregarded, and theprocess preceding the potential root process in the directed processdependency graph, if any, may be considered a root process. In FIG. 10,for instance, web browser 1035 may correspond to a known clientapplication process. Consequently, web browser 1035 is not considered tobe a root process and is disregarded, while the process preceding it inthe process dependency graph—web server 1050—may be considered a rootprocess.

It may also be desirable to exclude processes that are known tocorrespond to a non-business application. Infrastructure applicationssuch as backup systems, antivirus programs, and/or systems managementagents, for instance, may be considered non-business applications. Thus,in some embodiments, known non-business application processes which arenot to be associated with business applications may be identified, forexample, in a configuration file external to health data processingapplication 100. If a root process being considered by health dataprocessing application 100 corresponds to a known non-businessapplication process, the potential root process and the processesdepending on it, if any, are disregarded. For example, in FIG. 10,antivirus server 1020 may correspond to a known non-business applicationprocess. As a result, antivirus server 1020 and antivirus client 1040will be disregarded by health data processing application 100 inidentifying business applications.

As noted above, in tracing a dependency path, health data processingapplication may encounter a cycle of dependencies. For instance, as seenin FIG. 10, the dependencies between web server 1005, web server 1010,and web server 1015 constitute a cycle. Accordingly, in someembodiments, health data processing application 100 may use, e.g., theStrongly Connected Components algorithm to resolve the cycle in a stablemanner.

After identifying the root processes for the first process, health dataprocessing application 100 may group the root processes into one or moreroot groups. The grouping of root processes may be based, for example,on similarity heuristics that are applied to the configuration datacollected for each of the root processes. The similarity heuristics mayprovide, for instance, that multiple web servers serving the same webaddress are to be grouped together, that database instances that servicethe same database cluster are to be grouped together, and/or thatapplication server instances that belong to the same cluster asindicated by their command line instructions or configuration files areto be grouped together. In some embodiments, the similarity heuristicsand/or configuration data may be ranked according to priority, such thatparticular heuristics and/or configuration data are given precedence indetermining the grouping of root processes. The similarity heuristicsand/or configuration data may be provided, for instance, in filesexternal to health data processing application 100. In some embodiments,grouping the root processes into one or more root groups may occur onlyafter all processes corresponding to known transactional businessapplication processes are identified, and all root processes of thetransactional business application processes are identified.

Each root group may be given a name corresponding to the similarityheuristics and/or the collected configuration data on which the groupingis based. For example, a root group comprised of web servers servicingthe same web address may be assigned that web address as a name. Apolicy may be implemented to automatically update the membership of aroot group based on the similarity heuristics and/or collectedconfiguration data. For instance, if a new web server is brought onlineto service the same web address as a group of web servers alreadyassigned to a root group, a policy may ensure that the new web server isautomatically added to the existing root group.

Health data processing application 100 may identify a dependency chainfor the root processes in respective root groups. To identify adependency chain for a root process, health data processing application100 begins with the root process in the directed process dependencygraph, and then identifies the processes that are directly and/orindirectly depended upon by the root process. For instance, in FIG. 10,health data processing application 100 may have identified businessapplication front end 1030 as a root process. The dependency chain forbusiness application front end 1030 is then identified by following thechain of dependencies rightward in FIG. 10. Thus, the dependency chainfor business application front end 1030 may include app server 1045 onwhich business application front end 1030 depends, as well as databaseservers 1000 and 1055 on which app server 1045 depends. Taken together,the processes included in the dependency chain for the root processes ina root group may represent a business application.

Once the dependency chain for the root processes in a root group hasbeen identified, health data processing application 100 may aggregatethe performance metrics for the processes in the identified dependencychain. The aggregated performance metrics may be incorporated into themodel of network health generated by health data processing application100. The aggregated metrics may represent a summary of the performanceof a business application as a whole as perceived by the users of thebusiness application.

Reference is now made to FIG. 11, which illustrates exemplary operationscarried out by a health data processing application 100 in identifyingand monitoring related business application processes according to someembodiments of the present invention. At block 1100, health dataprocessing application 100 generates a directed process dependency graphthat includes multiple processes and the relationships between theprocesses. As discussed in greater detail above, a directed processdependency graph may be, e.g., a data construct including dataidentifying a plurality of processes and indicating the dependencyrelationships between processes. A first process in the directed processdependency graph that corresponds to a specified business applicationprocess is identified (block 1105). At block 1110, health dataprocessing application 100 traces a dependency path from the firstprocess to identify one or more root processes.

At least one of the one or more root processes is grouped into a rootgroup, based on similarity heuristics and/or collected configurationdata that is ranked by priority (block 1115). The root group is namedaccording to the similarity heuristics and/or collected configurationdata on which the grouping is based (block 1120). Health data processingapplication 100, at block 1125, implements a policy to automaticallyupdate the grouping of the root processes based on the similarityheuristics and/or collected configuration data. For instance, a policythat automatically recognizes a new process as belonging to an existinggroup and updates the group membership accordingly may be implemented.At block 1130, a dependency chain for the at least one root process inthe root group is identified, as indicated by the directed processdependency graph. Health data processing application 100 aggregates theperformance metrics for the processes in the identified dependency chain(block 1135).

Many variations and modifications can be made to the embodiments withoutsubstantially departing from the principles of the present invention.The following claims are provided to ensure that the present applicationmeets all statutory requirements as a priority application in alljurisdictions and shall not be construed as setting forth the scope ofthe present invention.

1. A method for identifying and monitoring related business applicationprocesses on at least one networked device, the method comprising:identifying a first process that corresponds to a specified businessapplication process; identifying one or more root processes that dependdirectly and/or indirectly on the first process, and are depended uponby no other process; grouping at least one of the one or more rootprocesses into a root group; identifying a dependency chain of the atleast one root process in the root group, comprising the at least oneroot process in the root group and one or more processes directly and/orindirectly depended upon by the at least one root process in the rootgroup; aggregating performance metrics for the processes in thedependency chain; and generating a directed process dependency graphthat includes a plurality of processes and relationships therebetween;wherein identifying the first process comprises identifying the firstprocess in the directed process dependency graph that corresponds to thespecified business application process, wherein identifying one or moreroot processes comprises identifying in the directed process dependencygraph one or more root processes by tracing a dependency path from thefirst process to a respective one of the one or more root processes, andwherein identifying the dependency chain of the at least one rootprocess in the root group comprises identifying a dependency chain asindicated by the directed process dependency graph; wherein identifyinga process, identifying one or more root processes, grouping at least oneof the one or more root processes, identifying a dependency chain, andaggregating the performance metrics comprise operations performed usingat least one programmed computer processor circuit.
 2. A method foridentifying and monitoring related business application processes on atleast one networked device, the method comprising: identifying a firstprocess that corresponds to a specified business application process;identifying one or more root processes by tracing a dependency path fromthe first process to a respective one of the one or more root processes;grouping at least one of the one or more root processes into a rootgroup; identifying a dependency chain of the at least one root processin the root group; aggregating performance metrics for the processes inthe dependency chain; and generating a directed process dependency graphthat includes a plurality of processes and relationships therebetween;wherein identifying the first process comprises identifying the firstprocess in the directed process dependency graph that corresponds to thespecified business application process, wherein identifying one or moreroot processes comprises identifying in the directed process dependencygraph one or more root processes by tracing a dependency path from thefirst process to a respective one of the one or more root processes, andwherein identifying the dependency chain of the at least one rootprocess in the root group comprises identifying a dependency chain asindicated by the directed process dependency graph; wherein identifyinga first process, identifying one or more root processes, grouping atleast one or more root processes, identifying a dependency chain, andaggregating performance metrics comprise operations performed using atleast one programmed computer processor circuit.
 3. The method of claim2, wherein the respective one of the one or more root processes dependsdirectly and/or indirectly on the first process, and is depended upon byno other process, and the dependency chain comprises the at least oneroot process in the root group and one or more processes directly and/orindirectly depended upon by the at least one root process in the rootgroup.
 4. The method of claim 2, wherein tracking the dependency pathcomprises following a chain in the directed process dependency graphbeginning with the first process and proceeding to one or moreintermediate processes depending directly and/or indirectly on the firstprocess as indicated by the directed process dependency graph.
 5. Themethod of claim 4, further comprising disregarding a second processidentified as a root process and designating a third process precedingthe second process in the chain as a root process, responsive to thesecond process corresponding to a specified client application process.6. The method of claim 4, further comprising disregarding a secondprocess identified as a root process, responsive to the second processcorresponding to a specified non-business application process.
 7. Themethod of claim 4, further comprising, responsive to detecting a cyclein the chain, using a Strongly Connected Components algorithm to resolvethe cycle.
 8. The method of claim 2, wherein grouping at least one ofthe one or more root processes into a root group comprises groupingbased on similarity heuristics and/or collected configuration data. 9.The method of claim 8, further comprising naming the root groupaccording to the similarity heuristics and/or collected configurationdata on which grouping is based.
 10. The method of claim 8, furthercomprising implementing a policy to automatically update grouping of theat least one of the one or more root processes based on the similarityheuristics and/or collected configuration data.
 11. The method of claim8, wherein the similarity heuristics and/or collected configuration dataare ranked by priority.
 12. The method of claim 2, wherein identifyingthe first process comprises identifying all currently executingprocesses that correspond to respective specified business applicationprocesses, wherein identifying one or more root processes comprisesidentifying one or more root processes for identified currentlyexecuting processes, prior to grouping the one or more root processes,and wherein grouping at least one of the one or more root processes intoa root group comprises grouping at least one of the one or more rootprocesses into one or more root groups.
 13. A computer program productcomprising: a non-transitory computer readable storage medium havingcomputer readable program code embodied therein, the computer readableprogram code comprising: computer readable program code configured toidentify a first process that corresponds to a specified businessapplication process; computer readable program code configured toidentify one or more root processes by tracing a dependency path fromthe first process to a respective one of the one or more root processes;computer readable program code configured to group at least one of theone or more root processes into a root group; computer readable programcode configured to identify a dependency chain of the at least one rootprocess in the root group; and computer readable program code configuredto aggregate performance metrics for the processes in the dependencychain; computer readable program code configured to generate a directedprocess dependency graph that includes a plurality of processes andrelationships therebetween; wherein the computer readable program codeconfigured to identify the first process comprises computer readableprogram code configured to identify the first process in the directedprocess dependency graph that corresponds to the specified businessapplication process, wherein computer readable program code configuredto identify one or more root processes comprises computer readableprogram code configured to identify in the directed process dependencygraph one or more root processes by tracing a dependency path from thefirst process to a respective one of the one or more root processes, andwherein computer readable program code configured to identify thedependency chain of the at least one root process in the root groupcomprises computer readable program code configured to identify adependency chain as indicated by the directed process dependency graph.14. The computer program product of claim 13, wherein the respective oneof the one or more root processes depends directly and/or indirectly onthe first process, and is depended upon by no other process, and thedependency chain comprises the at least one root process in the rootgroup and one or more processes directly and/or indirectly depended uponby the at least one root process in the root group.
 15. The computerprogram product of claim 13, the computer readable program codeconfigured to identify one or more root process by tracing thedependency path comprises: computer readable program code configured tofollow a chain in the directed process dependency graph beginning withthe first process; and computer readable program code configured toproceed to one or more intermediate processes depending directly and/orindirectly on the first process as indicated by the directed processdependency graph.
 16. The computer program product of claim 15, thecomputer readable program code further comprising computer readableprogram code configured to disregard a second process identified as aroot process and to designate a third process preceding the secondprocess in the chain as a root process, responsive to the second processcorresponding to a specified client application process.
 17. Thecomputer program product of claim 15, the computer readable program codefurther comprising computer readable program code configured todisregard a second process identified as a root process, responsive tothe second process corresponding to a specified non-business applicationprocess.
 18. The computer program product of claim 15, the computerreadable program code further comprising computer readable program codeconfigured to use, responsive to detecting a cycle in the chain, aStrongly Connected Components algorithm to resolve the cycle.
 19. Thecomputer program product of claim 13, wherein the computer readableprogram code configured to group at least one of the one or more rootprocesses into a root group comprises computer readable program codeconfigured to group based on similarity heuristics and/or collectedconfiguration data.
 20. The computer program product of claim 19, thecomputer readable program code further comprising computer readable codeconfigured to name the root group according to the similarity heuristicsand/or collected configuration data on which grouping is based.
 21. Thecomputer program product of claim 19, the computer readable program codefurther comprising computer readable code configured to implement apolicy to automatically update grouping of the at least one of the oneor more root processes based on the similarity heuristics and/orcollected configuration data.
 22. The computer program product of claim19, wherein the similarity heuristics and/or collected configurationdata are ranked by priority.
 23. The computer program product of claim13, wherein the computer readable program code configured to identify afirst process comprises computer readable program code configured toidentify all currently executing processes that correspond to respectivespecified business application processes, wherein the computer readableprogram code configured to identify one or more root processes comprisescomputer readable program code configured to identify one or more rootprocesses for identified currently executing processes, prior togrouping the one or more root processes, and wherein the computerreadable program code configured to group at least one of the one ormore root processes into a root group comprises computer readableprogram code configured to group at least one of the one or more rootprocesses into one or more root groups.